# imports
import act
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
import pyart
from datetime import timedelta

import cmweather
import pandas as pd
import glob

from bokeh.models.formatters import DatetimeTickFormatter
import hvplot.xarray
import holoviews as hv
hv.extension("bokeh")

## You are using the Python ARM Radar Toolkit (Py-ART), an open source
## library for working with weather radar data. Py-ART is partly
## supported by the U.S. Department of Energy as part of the Atmospheric
## Radiation Measurement (ARM) Climate Research Facility, an Office of
## Science user facility.
##
## If you use this software to prepare a publication, please cite:
##
##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119

ERROR 1: PROJ: proj_create_from_database: Open of /opt/conda/share/proj failed

# Set your username and token here!
username = 'jeissner'
token = '196301151e10a63'

# COMBLE ARSCL datastream 
datastream = 'anxarsclkazr1kolliasM1.c1'

startdate = '2020-03-01'
enddate = '2020-03-31'

# Read in data
result = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)

ds_arscl = act.io.read_arm_netcdf(result)

ds_arscl

[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200305.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200304.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200306.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200307.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200328.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200301.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200302.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200303.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200324.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200326.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200309.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200321.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200320.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200323.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200318.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200331.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200319.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200313.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200329.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200325.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200327.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200308.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200322.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200330.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200311.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200310.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200312.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200315.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200317.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200316.000000.nc
[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200314.000000.nc

If you use these data to prepare a publication, please cite:

Johnson, K., Jensen, M., & Giangrande, S. Active Remote Sensing of CLouds
(ARSCL) product using Ka-band ARM Zenith Radars (ARSCLKAZR1KOLLIAS). Atmospheric
Radiation Measurement (ARM) User Facility. https://doi.org/10.5439/1228768

<xarray.Dataset> Size: 34GB
Dimensions:                               (time: 669600, height: 596,
                                           layer: 10, radar_mode: 4)
Coordinates:
  * time                                  (time) datetime64[ns] 5MB 2020-03-0...
  * layer                                 (layer) int32 40B 0 1 2 3 4 5 6 7 8 9
  * height                                (height) float32 2kB 160.0 ... 1.80...
  * radar_mode                            (radar_mode) |S2 8B b'hi' ... b'pr'
Data variables: (12/33)
    base_time                             (time) datetime64[ns] 5MB 2020-03-0...
    time_offset                           (time) datetime64[ns] 5MB 2020-03-0...
    reflectivity_best_estimate            (time, height) float32 2GB dask.array<chunksize=(901, 596), meta=np.ndarray>
    qc_reflectivity_best_estimate         (time, height) int32 2GB dask.array<chunksize=(901, 596), meta=np.ndarray>
    reflectivity                          (time, height) float32 2GB dask.array<chunksize=(901, 596), meta=np.ndarray>
    qc_reflectivity                       (time, height) int32 2GB dask.array<chunksize=(901, 596), meta=np.ndarray>
    ...                                    ...
    minimum_detectable_reflectivity_flag  (time, height) float32 2GB dask.array<chunksize=(901, 596), meta=np.ndarray>
    reflectivity_saturation_flag          (time, height) float32 2GB dask.array<chunksize=(901, 596), meta=np.ndarray>
    instrument_availability_flag          (time) int16 1MB dask.array<chunksize=(900,), meta=np.ndarray>
    lat                                   (time) float32 3MB 69.14 ... 69.14
    lon                                   (time) float32 3MB 15.68 ... 15.68
    alt                                   (time) float32 3MB 2.0 2.0 ... 2.0 2.0
Attributes: (12/22)
    command_line:                     idl -R -n kazrcfrarscl -n kazrcfrarsclc...
    Conventions:                      ARM-1.2
    process_version:                  vap-kazrcfrarscl-1.6-4.el7
    dod_version:                      arsclkazr1kollias-c1-4.0
    site_id:                          anx
    platform_id:                      arsclkazr1kollias
    ...                               ...
    doi:                              10.5439/1228768
    history:                          created by user malynn on machine node1...
    _file_dates:                      ['20200301', '20200302', '20200303', '2...
    _file_times:                      ['000000', '000000', '000000', '000000'...
    _datastream:                      anxarsclkazr1kolliasM1.c1
    _arm_standards_flag:              1

xarray.Dataset

• Dimensions:
  • time: 669600
  • height: 596
  • layer: 10
  • radar_mode: 4
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2020-03-01 ... 2020-03-31T23:59:56

    long_name :

    Time offset from midnight

    standard_name :

    time

    array(['2020-03-01T00:00:00.000000000', '2020-03-01T00:00:04.000000000',
           '2020-03-01T00:00:08.000000000', ..., '2020-03-31T23:59:48.000000000',
           '2020-03-31T23:59:52.000000000', '2020-03-31T23:59:56.000000000'],
          dtype='datetime64[ns]')

  • layer
    (layer)
    int32
    0 1 2 3 4 5 6 7 8 9

    long_name :

    Cloud layer number

    units :

    unitless

    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int32)

  • height
    (height)
    float32
    160.0 190.0 ... 1.798e+04 1.801e+04

    long_name :

    Height above ground level

    units :

    m

    standard_name :

    height

    array([  160.,   190.,   220., ..., 17950., 17980., 18010.], dtype=float32)

  • radar_mode
    (radar_mode)
    |S2
    b'hi' b'md' b'ge' b'pr'

    long_name :

    Radar mode names

    units :

    unitless

    comment :

    Only two radar modes are in use simultaneously. See global attribute radar_modes_in_use for list of modes used

    array([b'hi', b'md', b'ge', b'pr'], dtype='|S2')

• Data variables: (33)
  • base_time
    (time)
    datetime64[ns]
    2020-03-01 ... 2020-03-31

    string :

    2020-03-01 00:00:00 0:00

    long_name :

    Base time in Epoch

    ancillary_variables :

    time_offset

    array(['2020-03-01T00:00:00.000000000', '2020-03-01T00:00:00.000000000',
           '2020-03-01T00:00:00.000000000', ...,
           '2020-03-31T00:00:00.000000000', '2020-03-31T00:00:00.000000000',
           '2020-03-31T00:00:00.000000000'], dtype='datetime64[ns]')

  • time_offset
    (time)
    datetime64[ns]
    2020-03-01 ... 2020-03-31T23:59:56

    long_name :

    Time offset from base_time

    ancillary_variables :

    base_time

    array(['2020-03-01T00:00:00.000000000', '2020-03-01T00:00:04.000000000',
           '2020-03-01T00:00:08.000000000', ...,
           '2020-03-31T23:59:48.000000000', '2020-03-31T23:59:52.000000000',
           '2020-03-31T23:59:56.000000000'], dtype='datetime64[ns]')

  • reflectivity_best_estimate
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Best-estimate reflectivity

    units :

    dBZ

    ancillary_variables :

    qc_reflectivity_best_estimate

    valid_min :

    -90.0

    valid_max :

    50.0

    resolution :

    0.001

    comment :

    Contains data from best radar operating mode (see radar_mode_flag); data points with non-missing values contain hydrometeors or both hydrometeors and clutter (see reflectivity_clutter_flag)

    Array Chunk Bytes 1.49 GiB 2.05 MiB Shape (669600, 596) (901, 596) Dask graph 744 chunks in 63 graph layers Data type float32 numpy.ndarray

  • qc_reflectivity_best_estimate
    (time, height)
    int32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Quality check results on field: Best-estimate reflectivity

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is less than the valid_min.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is greater than the valid_max.

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 1.49 GiB 2.05 MiB Shape (669600, 596) (901, 596) Dask graph 744 chunks in 63 graph layers Data type int32 numpy.ndarray

  • reflectivity
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Reflectivity

    units :

    dBZ

    ancillary_variables :

    qc_reflectivity

    valid_min :

    -90.0

    valid_max :

    50.0

    resolution :

    0.001

    comment :

    Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter

    Array Chunk Bytes 1.49 GiB 2.05 MiB Shape (669600, 596) (901, 596) Dask graph 744 chunks in 63 graph layers Data type float32 numpy.ndarray

  • qc_reflectivity
    (time, height)
    int32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Quality check results on field: Reflectivity

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is less than the valid_min.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is greater than the valid_max.

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 1.49 GiB 2.05 MiB Shape (669600, 596) (901, 596) Dask graph 744 chunks in 63 graph layers Data type int32 numpy.ndarray

  • mean_doppler_velocity
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Mean Doppler velocity

    units :

    m/s

    ancillary_variables :

    qc_mean_doppler_velocity

    valid_min :

    -25.0

    valid_max :

    25.0

    resolution :

    0.001

    positive :

    up

    comment :

    Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter

    Array Chunk Bytes 1.49 GiB 2.05 MiB Shape (669600, 596) (901, 596) Dask graph 744 chunks in 63 graph layers Data type float32 numpy.ndarray

  • qc_mean_doppler_velocity
    (time, height)
    int32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Quality check results on field: Mean Doppler velocity

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is less than the valid_min.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is greater than the valid_max.

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 1.49 GiB 2.05 MiB Shape (669600, 596) (901, 596) Dask graph 744 chunks in 63 graph layers Data type int32 numpy.ndarray

  • spectral_width
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Spectral width

    units :

    m/s

    ancillary_variables :

    qc_spectral_width

    valid_min :

    0.0

    valid_max :

    10.0

    resolution :

    0.001

    comment :

    Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter

    Array Chunk Bytes 1.49 GiB 2.05 MiB Shape (669600, 596) (901, 596) Dask graph 744 chunks in 63 graph layers Data type float32 numpy.ndarray

  • qc_spectral_width
    (time, height)
    int32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Quality check results on field: Spectral width

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is less than the valid_min.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is greater than the valid_max.

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 1.49 GiB 2.05 MiB Shape (669600, 596) (901, 596) Dask graph 744 chunks in 63 graph layers Data type int32 numpy.ndarray

  • linear_depolarization_ratio
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Linear depolarization ratio

    units :

    dBZ

    ancillary_variables :

    qc_linear_depolarization_ratio

    valid_min :

    -50.0

    valid_max :

    50.0

    comment :

    Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter

    Array Chunk Bytes 1.49 GiB 2.05 MiB Shape (669600, 596) (901, 596) Dask graph 744 chunks in 63 graph layers Data type float32 numpy.ndarray

  • qc_linear_depolarization_ratio
    (time, height)
    int32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Quality check results on field: Linear depolarization ratio

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is less than the valid_min.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is greater than the valid_max.

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 1.49 GiB 2.05 MiB Shape (669600, 596) (901, 596) Dask graph 744 chunks in 63 graph layers Data type int32 numpy.ndarray

  • signal_to_noise_ratio
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Signal-to-noise ratio

    units :

    dB

    resolution :

    0.001

    comment :

    Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter

    Array Chunk Bytes 1.49 GiB 2.05 MiB Shape (669600, 596) (901, 596) Dask graph 744 chunks in 63 graph layers Data type float32 numpy.ndarray

  • reflectivity_clutter_flag
    (time, height)
    int16
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Reflectivity clutter flag

    units :

    unitless

    flag_values :

    [ 0 1 2 3 9 10]

    flag_meanings :

    No_significant_detection Hydrometeor-only_detection Both_hydrometeor_and_clutter_detection Clutter-only_detection Bad_data Missing_data

    flag_0_description :

    No significant detection

    flag_1_description :

    Hydrometeor-only detection

    flag_2_description :

    Both hydrometeor and clutter detection

    flag_3_description :

    Clutter-only detection

    flag_9_description :

    Bad data

    flag_10_description :

    Missing data

    Array Chunk Bytes 761.19 MiB 1.02 MiB Shape (669600, 596) (901, 596) Dask graph 744 chunks in 63 graph layers Data type int16 numpy.ndarray

  • mean_doppler_velocity_dealias_flag
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Indication of whether or not dealiasing was performed on the mean_doppler_velocity

    units :

    unitless

    flag_values :

    [0 1]

    flag_meanings :

    no_dealias dealias

    flag_0_description :

    No dealiasing performed

    flag_1_description :

    Dealiasing performed

    Array Chunk Bytes 1.49 GiB 2.05 MiB Shape (669600, 596) (901, 596) Dask graph 744 chunks in 63 graph layers Data type float32 numpy.ndarray

  • radar_mode_flag
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Radar mode flag

    units :

    unitless

    flag_values :

    [0 1 2 3 4]

    flag_meanings :

    No_significant_detection High_sensitivity_mode-long_pulse Medium_sensitivity_mode-long_pulse General_mode-short_pulse Precipitation_mode-short_pulse-attenuated

    flag_0_description :

    No significant detection

    flag_1_description :

    High sensitivity mode (long pulse)

    flag_2_description :

    Medium sensitivity mode (long pulse)

    flag_3_description :

    General mode (short pulse)

    flag_4_description :

    Precipitation mode (short pulse, attenuated)

    Array Chunk Bytes 1.49 GiB 2.05 MiB Shape (669600, 596) (901, 596) Dask graph 744 chunks in 63 graph layers Data type float32 numpy.ndarray

  • cloud_source_flag
    (time, height)
    int16
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Instrument source flag for cloud (hydrometeor) detections

    units :

    unitless

    flag_values :

    [0 1 2 3 4 5 6]

    flag_meanings :

    No_detection_due_to_missing_radar_and_micropulse_lidar_data clear_according_to_radar_and_lidar cloud_detected_by_radar_and_lidar cloud_detected_by_radar_only cloud_detected_by_lidar_only cloud_detected_by_radar_but_lidar_data_missing cloud_detected_by_lidar_but_radar_data_missing

    flag_0_description :

    No detection due to missing radar and micropulse lidar data

    flag_1_description :

    Clear according to radar and lidar

    flag_2_description :

    Cloud detected by radar and lidar

    flag_3_description :

    Cloud detected by radar only

    flag_4_description :

    Cloud detected by lidar only

    flag_5_description :

    Cloud detected by radar but lidar data missing

    flag_6_description :

    Cloud detected by lidar but radar data missing

    Array Chunk Bytes 761.19 MiB 1.02 MiB Shape (669600, 596) (901, 596) Dask graph 744 chunks in 63 graph layers Data type int16 numpy.ndarray

  • precip_mean
    (time)
    float32
    dask.array<chunksize=(900,), meta=np.ndarray>

    long_name :

    Precipitation mean from rain gauge

    units :

    mm/hr

    ancillary_variables :

    qc_precip_mean

    Array Chunk Bytes 2.55 MiB 3.52 kiB Shape (669600,) (900,) Dask graph 744 chunks in 63 graph layers Data type float32 numpy.ndarray

  • qc_precip_mean
    (time)
    int32
    dask.array<chunksize=(900,), meta=np.ndarray>

    long_name :

    Quality check results on field: Precipitation mean from rain gauge

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Not used

    bit_1_assessment :

    Bad

    bit_2_description :

    Not used

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 2.55 MiB 3.52 kiB Shape (669600,) (900,) Dask graph 744 chunks in 63 graph layers Data type int32 numpy.ndarray

  • mwr_lwp
    (time)
    float32
    dask.array<chunksize=(900,), meta=np.ndarray>

    long_name :

    Liquid water path best-estimate from microwave radiometer

    units :

    g/m^2

    ancillary_variables :

    qc_mwr_lwp

    Array Chunk Bytes 2.55 MiB 3.52 kiB Shape (669600,) (900,) Dask graph 744 chunks in 63 graph layers Data type float32 numpy.ndarray

  • qc_mwr_lwp
    (time)
    int32
    dask.array<chunksize=(900,), meta=np.ndarray>

    long_name :

    Quality check results on field: Liquid water path best-estimate from microwave radiometer

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Not used

    bit_1_assessment :

    Bad

    bit_2_description :

    Not used

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 2.55 MiB 3.52 kiB Shape (669600,) (900,) Dask graph 744 chunks in 63 graph layers Data type int32 numpy.ndarray

  • radar_first_top
    (time)
    float32
    dask.array<chunksize=(900,), meta=np.ndarray>

    long_name :

    KAZR top height of lowest significant detection layer, before clutter removal

    units :

    m

    valid_range :

    [ 0. 25000.]

    flag_values :

    -1.0

    flag_meanings :

    clear_sky

    Array Chunk Bytes 2.55 MiB 3.52 kiB Shape (669600,) (900,) Dask graph 744 chunks in 63 graph layers Data type float32 numpy.ndarray

  • cloud_base_best_estimate
    (time)
    float32
    dask.array<chunksize=(900,), meta=np.ndarray>

    long_name :

    Cloud base best estimate, based on ceilometer and micropulse lidar

    units :

    m

    valid_range :

    [ 0. 25000.]

    flag_values :

    [-2. -1.]

    flag_meanings :

    possible_clear_sky clear_sky

    comment :

    -2. Possible clear sky (No MPL observations available, Ceilometer obscured, but no cloud detected), -1. Clear sky, >= 0. Valid cloud base height

    Array Chunk Bytes 2.55 MiB 3.52 kiB Shape (669600,) (900,) Dask graph 744 chunks in 63 graph layers Data type float32 numpy.ndarray

  • cloud_layer_base_height
    (time, layer)
    float32
    dask.array<chunksize=(900, 10), meta=np.ndarray>

    long_name :

    Base height of hydrometeor layers for up to 10 layers, based on combined radar and micropulse lidar observations

    units :

    m

    valid_range :

    [ 0. 25000.]

    flag_values :

    -1.0

    flag_meanings :

    clear_sky

    Array Chunk Bytes 25.54 MiB 35.16 kiB Shape (669600, 10) (900, 10) Dask graph 744 chunks in 63 graph layers Data type float32 numpy.ndarray

  • cloud_layer_top_height
    (time, layer)
    float32
    dask.array<chunksize=(900, 10), meta=np.ndarray>

    long_name :

    Top height of hydrometeor layers for up to 10 layers, based on combined radar and micropulse lidar observations

    units :

    m

    valid_range :

    [ 0. 25000.]

    flag_values :

    -1.0

    flag_meanings :

    clear_sky

    Array Chunk Bytes 25.54 MiB 35.16 kiB Shape (669600, 10) (900, 10) Dask graph 744 chunks in 63 graph layers Data type float32 numpy.ndarray

  • cloud_mask_mplzwang
    (time, height)
    float32
    dask.array<chunksize=(900, 596), meta=np.ndarray>

    long_name :

    Cloud mask from 30smpl1zwang

    units :

    unitless

    flag_values :

    [0 1 2]

    flag_meanings :

    clear cloudy mask_not_used_to_determine_cloud_boundaries_below_3km

    flag_0_description :

    clear

    flag_1_description :

    cloudy

    flag_2_description :

    Mask not used to determine cloud boundaries below 3km

    comment :

    Cloud mask indeterminate below 500 m

    Array Chunk Bytes 1.49 GiB 2.05 MiB Shape (669600, 596) (900, 596) Dask graph 744 chunks in 63 graph layers Data type float32 numpy.ndarray

  • minimum_detectable_reflectivity
    (time, radar_mode, height)
    float32
    dask.array<chunksize=(21600, 4, 596), meta=np.ndarray>

    long_name :

    Minimum detectable reflectivity level

    units :

    dBZ

    comment :

    Noise values are reduced by signal processing, which is dependent upon the cloud properties. Therefore actual noise levels can fluctuate with time. Reported here are mean values for the time period.

    Array Chunk Bytes 5.95 GiB 196.44 MiB Shape (669600, 4, 596) (21600, 4, 596) Dask graph 31 chunks in 94 graph layers Data type float32 numpy.ndarray

  • minimum_detectable_reflectivity_flag
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Flag identifies points with reflectivity below radar expected sensitivity level

    units :

    unitless

    flag_values :

    [0 1]

    flag_meanings :

    reflectivity_exceeds_minimum_detectable_level reflectivity_below_minimum_sensitivity

    flag_0_description :

    Reflectivity exceeds minimum detectable level

    flag_1_description :

    Reflectivity below minimum sensitivity

    Array Chunk Bytes 1.49 GiB 2.05 MiB Shape (669600, 596) (901, 596) Dask graph 744 chunks in 63 graph layers Data type float32 numpy.ndarray

  • reflectivity_saturation_flag
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Flag indicating when reflectivity values are saturated

    units :

    unitless

    flag_values :

    [0 1]

    flag_meanings :

    reflectivity_not_saturated reflectivity_at_or_above_radar_saturation_level

    flag_0_description :

    Reflectivity not saturated

    flag_1_description :

    Reflectivity at or above radar saturation level

    Array Chunk Bytes 1.49 GiB 2.05 MiB Shape (669600, 596) (901, 596) Dask graph 744 chunks in 63 graph layers Data type float32 numpy.ndarray

  • instrument_availability_flag
    (time)
    int16
    dask.array<chunksize=(900,), meta=np.ndarray>

    long_name :

    Indicates which instruments have data available

    units :

    unitless

    flag_masks :

    [ 1 2 4 8 16]

    flag_meanings :

    KAZR MPL Ceilometer MWR rain_gauge

    bit_1_description :

    KAZR

    bit_2_description :

    MPL

    bit_3_description :

    Ceilometer

    bit_4_description :

    MWR

    bit_5_description :

    rain gauge

    Array Chunk Bytes 1.28 MiB 1.76 kiB Shape (669600,) (900,) Dask graph 744 chunks in 63 graph layers Data type int16 numpy.ndarray

  • lat
    (time)
    float32
    69.14 69.14 69.14 ... 69.14 69.14

    long_name :

    North latitude

    units :

    degree_N

    valid_min :

    -90.0

    valid_max :

    90.0

    standard_name :

    latitude

    array([69.14128, 69.14128, 69.14128, ..., 69.14128, 69.14128, 69.14128],
          dtype=float32)

  • lon
    (time)
    float32
    15.68 15.68 15.68 ... 15.68 15.68

    long_name :

    East longitude

    units :

    degree_E

    valid_min :

    -180.0

    valid_max :

    180.0

    standard_name :

    longitude

    array([15.684167, 15.684167, 15.684167, ..., 15.684167, 15.684167,
           15.684167], dtype=float32)

  • alt
    (time)
    float32
    2.0 2.0 2.0 2.0 ... 2.0 2.0 2.0 2.0

    long_name :

    Altitude above mean sea level

    units :

    m

    standard_name :

    altitude

    array([2., 2., 2., ..., 2., 2., 2.], dtype=float32)

• Indexes: (4)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-03-01 00:00:00', '2020-03-01 00:00:04',
                   '2020-03-01 00:00:08', '2020-03-01 00:00:12',
                   '2020-03-01 00:00:16', '2020-03-01 00:00:20',
                   '2020-03-01 00:00:24', '2020-03-01 00:00:28',
                   '2020-03-01 00:00:32', '2020-03-01 00:00:36',
                   ...
                   '2020-03-31 23:59:20', '2020-03-31 23:59:24',
                   '2020-03-31 23:59:28', '2020-03-31 23:59:32',
                   '2020-03-31 23:59:36', '2020-03-31 23:59:40',
                   '2020-03-31 23:59:44', '2020-03-31 23:59:48',
                   '2020-03-31 23:59:52', '2020-03-31 23:59:56'],
                  dtype='datetime64[ns]', name='time', length=669600, freq=None))

  • layer
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int32', name='layer'))

  • height
    PandasIndex

    PandasIndex(Index([  160.0,   190.0,   220.0,   250.0,   280.0,   310.0,   340.0,   370.0,
             400.0,   430.0,
           ...
           17740.0, 17770.0, 17800.0, 17830.0, 17860.0, 17890.0, 17920.0, 17950.0,
           17980.0, 18010.0],
          dtype='float32', name='height', length=596))

  • radar_mode
    PandasIndex

    PandasIndex(Index([b'hi', b'md', b'ge', b'pr'], dtype='object', name='radar_mode'))

• Attributes: (22)

  command_line :

  idl -R -n kazrcfrarscl -n kazrcfrarsclc1 -s anx -f M1 -b 20200301 --asynchronous

  Conventions :

  ARM-1.2

  process_version :

  vap-kazrcfrarscl-1.6-4.el7

  dod_version :

  arsclkazr1kollias-c1-4.0

  site_id :

  anx

  platform_id :

  arsclkazr1kollias

  facility_id :

  M1

  data_level :

  c1

  location_description :

  Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE), Andoy, Norway

  datastream :

  anxarsclkazr1kolliasM1.c1

  input_datastreams :

  anxceilM1.b1 : 1.6 : 20200301.000014 anxkazrcfrcorgeM1.c1 : 1.4 : 20200301.000000 anxkazrcfrcormdM1.c1 : 1.4 : 20200301.000000 anx30smplcmask1zwangM1.c1 : 1.0 : 20200301.000004 anxmwrret1liljclouM1.c2 : 1.6 : 20200301.000008 anxmwrlosM1.b1 : 2.3 : 20200301.000008-20200301.000105 anxmetM1.b1 : 4.41 : 20200301.000000

  radar_polarization_status :

  single polarization

  radar_modes_in_use :

  md ge

  maximum_clutter_height :

  Not applicable at this site

  radar_operating_frequency_burst :

  34.830 GHz

  radar_operating_frequency_chirp :

  34.890 GHz

  doi :

  10.5439/1228768

  history :

  created by user malynn on machine node1.adc.arm.gov at 2022-12-06 22:14:05, using vap-kazrcfrarscl-1.6-4.el7

  _file_dates :

  ['20200301', '20200302', '20200303', '20200304', '20200305', '20200306', '20200307', '20200308', '20200309', '20200310', '20200311', '20200312', '20200313', '20200314', '20200315', '20200316', '20200317', '20200318', '20200319', '20200320', '20200321', '20200322', '20200323', '20200324', '20200325', '20200326', '20200327', '20200328', '20200329', '20200330', '20200331']

  _file_times :

  ['000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000']

  _datastream :

  anxarsclkazr1kolliasM1.c1

  _arm_standards_flag :

  1

%store -r

time_s[0]
vdates = []
for time in time_s:
    time = str(time)
    dates = time[0:10]
    vdates.append(dates)

    ds2 = ds_arscl.sel(time=slice(dates))
    
    ref = ds2.reflectivity_best_estimate
    ref_lowest_5000m = ref.sel(height=slice(0., 5000))
    ref_lowest_5000m.plot(x='time',y='height',
                                   cmap='ChaseSpectral',
                                   vmin=-40,
                                   vmax=20)
    plt.show()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[9], line 12
     10 ref = ds2.reflectivity_best_estimate
     11 ref_lowest_5000m = ref.sel(height=slice(0., 5000))
---> 12 ref_lowest_5000m.plot(x='time',y='height',
     13                                cmap='ChaseSpectral',
     14                                vmin=-40,
     15                                vmax=20)
     16 plt.show()

File /opt/conda/lib/python3.11/site-packages/xarray/plot/accessor.py:48, in DataArrayPlotAccessor.__call__(self, **kwargs)
     46 @functools.wraps(dataarray_plot.plot, assigned=("__doc__", "__annotations__"))
     47 def __call__(self, **kwargs) -> Any:
---> 48     return dataarray_plot.plot(self._da, **kwargs)

File /opt/conda/lib/python3.11/site-packages/xarray/plot/dataarray_plot.py:282, in plot(darray, row, col, col_wrap, ax, hue, subplot_kws, **kwargs)
    279 plotfunc: Callable
    281 if ndims == 0 or darray.size == 0:
--> 282     raise TypeError("No numeric data to plot.")
    283 if ndims in (1, 2):
    284     if row or col:

TypeError: No numeric data to plot.

ds2

<xarray.Dataset> Size: 2kB
Dimensions:                               (time: 0, height: 596, layer: 10,
                                           radar_mode: 4)
Coordinates:
  * time                                  (time) datetime64[ns] 0B 
  * layer                                 (layer) int32 40B 0 1 2 3 4 5 6 7 8 9
  * height                                (height) float32 2kB 160.0 ... 1.80...
  * radar_mode                            (radar_mode) |S2 8B b'hi' ... b'pr'
Data variables: (12/33)
    base_time                             (time) datetime64[ns] 0B 
    time_offset                           (time) datetime64[ns] 0B 
    reflectivity_best_estimate            (time, height) float32 0B dask.array<chunksize=(0, 596), meta=np.ndarray>
    qc_reflectivity_best_estimate         (time, height) int32 0B dask.array<chunksize=(0, 596), meta=np.ndarray>
    reflectivity                          (time, height) float32 0B dask.array<chunksize=(0, 596), meta=np.ndarray>
    qc_reflectivity                       (time, height) int32 0B dask.array<chunksize=(0, 596), meta=np.ndarray>
    ...                                    ...
    minimum_detectable_reflectivity_flag  (time, height) float32 0B dask.array<chunksize=(0, 596), meta=np.ndarray>
    reflectivity_saturation_flag          (time, height) float32 0B dask.array<chunksize=(0, 596), meta=np.ndarray>
    instrument_availability_flag          (time) int16 0B dask.array<chunksize=(0,), meta=np.ndarray>
    lat                                   (time) float32 0B 
    lon                                   (time) float32 0B 
    alt                                   (time) float32 0B 
Attributes: (12/22)
    command_line:                     idl -R -n kazrcfrarscl -n kazrcfrarsclc...
    Conventions:                      ARM-1.2
    process_version:                  vap-kazrcfrarscl-1.6-4.el7
    dod_version:                      arsclkazr1kollias-c1-4.0
    site_id:                          anx
    platform_id:                      arsclkazr1kollias
    ...                               ...
    doi:                              10.5439/1228768
    history:                          created by user malynn on machine node1...
    _file_dates:                      ['20200301', '20200302', '20200303', '2...
    _file_times:                      ['000000', '000000', '000000', '000000'...
    _datastream:                      anxarsclkazr1kolliasM1.c1
    _arm_standards_flag:              1

xarray.Dataset

• Dimensions:
  • time: 0
  • height: 596
  • layer: 10
  • radar_mode: 4
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]

    long_name :

    Time offset from midnight

    standard_name :

    time

    array([], dtype='datetime64[ns]')

  • layer
    (layer)
    int32
    0 1 2 3 4 5 6 7 8 9

    long_name :

    Cloud layer number

    units :

    unitless

    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int32)

  • height
    (height)
    float32
    160.0 190.0 ... 1.798e+04 1.801e+04

    long_name :

    Height above ground level

    units :

    m

    standard_name :

    height

    array([  160.,   190.,   220., ..., 17950., 17980., 18010.], dtype=float32)

  • radar_mode
    (radar_mode)
    |S2
    b'hi' b'md' b'ge' b'pr'

    long_name :

    Radar mode names

    units :

    unitless

    comment :

    Only two radar modes are in use simultaneously. See global attribute radar_modes_in_use for list of modes used

    array([b'hi', b'md', b'ge', b'pr'], dtype='|S2')

• Data variables: (33)
  • base_time
    (time)
    datetime64[ns]

    string :

    2020-03-01 00:00:00 0:00

    long_name :

    Base time in Epoch

    ancillary_variables :

    time_offset

    array([], dtype='datetime64[ns]')

  • time_offset
    (time)
    datetime64[ns]

    long_name :

    Time offset from base_time

    ancillary_variables :

    base_time

    array([], dtype='datetime64[ns]')

  • reflectivity_best_estimate
    (time, height)
    float32
    dask.array<chunksize=(0, 596), meta=np.ndarray>

    long_name :

    Best-estimate reflectivity

    units :

    dBZ

    ancillary_variables :

    qc_reflectivity_best_estimate

    valid_min :

    -90.0

    valid_max :

    50.0

    resolution :

    0.001

    comment :

    Contains data from best radar operating mode (see radar_mode_flag); data points with non-missing values contain hydrometeors or both hydrometeors and clutter (see reflectivity_clutter_flag)

    Array Chunk Bytes 0 B 0 B Shape (0, 596) (0, 596) Dask graph 1 chunks in 64 graph layers Data type float32 numpy.ndarray

  • qc_reflectivity_best_estimate
    (time, height)
    int32
    dask.array<chunksize=(0, 596), meta=np.ndarray>

    long_name :

    Quality check results on field: Best-estimate reflectivity

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is less than the valid_min.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is greater than the valid_max.

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 0 B 0 B Shape (0, 596) (0, 596) Dask graph 1 chunks in 64 graph layers Data type int32 numpy.ndarray

  • reflectivity
    (time, height)
    float32
    dask.array<chunksize=(0, 596), meta=np.ndarray>

    long_name :

    Reflectivity

    units :

    dBZ

    ancillary_variables :

    qc_reflectivity

    valid_min :

    -90.0

    valid_max :

    50.0

    resolution :

    0.001

    comment :

    Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter

    Array Chunk Bytes 0 B 0 B Shape (0, 596) (0, 596) Dask graph 1 chunks in 64 graph layers Data type float32 numpy.ndarray

  • qc_reflectivity
    (time, height)
    int32
    dask.array<chunksize=(0, 596), meta=np.ndarray>

    long_name :

    Quality check results on field: Reflectivity

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is less than the valid_min.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is greater than the valid_max.

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 0 B 0 B Shape (0, 596) (0, 596) Dask graph 1 chunks in 64 graph layers Data type int32 numpy.ndarray

  • mean_doppler_velocity
    (time, height)
    float32
    dask.array<chunksize=(0, 596), meta=np.ndarray>

    long_name :

    Mean Doppler velocity

    units :

    m/s

    ancillary_variables :

    qc_mean_doppler_velocity

    valid_min :

    -25.0

    valid_max :

    25.0

    resolution :

    0.001

    positive :

    up

    comment :

    Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter

    Array Chunk Bytes 0 B 0 B Shape (0, 596) (0, 596) Dask graph 1 chunks in 64 graph layers Data type float32 numpy.ndarray

  • qc_mean_doppler_velocity
    (time, height)
    int32
    dask.array<chunksize=(0, 596), meta=np.ndarray>

    long_name :

    Quality check results on field: Mean Doppler velocity

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is less than the valid_min.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is greater than the valid_max.

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 0 B 0 B Shape (0, 596) (0, 596) Dask graph 1 chunks in 64 graph layers Data type int32 numpy.ndarray

  • spectral_width
    (time, height)
    float32
    dask.array<chunksize=(0, 596), meta=np.ndarray>

    long_name :

    Spectral width

    units :

    m/s

    ancillary_variables :

    qc_spectral_width

    valid_min :

    0.0

    valid_max :

    10.0

    resolution :

    0.001

    comment :

    Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter

    Array Chunk Bytes 0 B 0 B Shape (0, 596) (0, 596) Dask graph 1 chunks in 64 graph layers Data type float32 numpy.ndarray

  • qc_spectral_width
    (time, height)
    int32
    dask.array<chunksize=(0, 596), meta=np.ndarray>

    long_name :

    Quality check results on field: Spectral width

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is less than the valid_min.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is greater than the valid_max.

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 0 B 0 B Shape (0, 596) (0, 596) Dask graph 1 chunks in 64 graph layers Data type int32 numpy.ndarray

  • linear_depolarization_ratio
    (time, height)
    float32
    dask.array<chunksize=(0, 596), meta=np.ndarray>

    long_name :

    Linear depolarization ratio

    units :

    dBZ

    ancillary_variables :

    qc_linear_depolarization_ratio

    valid_min :

    -50.0

    valid_max :

    50.0

    comment :

    Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter

    Array Chunk Bytes 0 B 0 B Shape (0, 596) (0, 596) Dask graph 1 chunks in 64 graph layers Data type float32 numpy.ndarray

  • qc_linear_depolarization_ratio
    (time, height)
    int32
    dask.array<chunksize=(0, 596), meta=np.ndarray>

    long_name :

    Quality check results on field: Linear depolarization ratio

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is less than the valid_min.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is greater than the valid_max.

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 0 B 0 B Shape (0, 596) (0, 596) Dask graph 1 chunks in 64 graph layers Data type int32 numpy.ndarray

  • signal_to_noise_ratio
    (time, height)
    float32
    dask.array<chunksize=(0, 596), meta=np.ndarray>

    long_name :

    Signal-to-noise ratio

    units :

    dB

    resolution :

    0.001

    comment :

    Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter

    Array Chunk Bytes 0 B 0 B Shape (0, 596) (0, 596) Dask graph 1 chunks in 64 graph layers Data type float32 numpy.ndarray

  • reflectivity_clutter_flag
    (time, height)
    int16
    dask.array<chunksize=(0, 596), meta=np.ndarray>

    long_name :

    Reflectivity clutter flag

    units :

    unitless

    flag_values :

    [ 0 1 2 3 9 10]

    flag_meanings :

    No_significant_detection Hydrometeor-only_detection Both_hydrometeor_and_clutter_detection Clutter-only_detection Bad_data Missing_data

    flag_0_description :

    No significant detection

    flag_1_description :

    Hydrometeor-only detection

    flag_2_description :

    Both hydrometeor and clutter detection

    flag_3_description :

    Clutter-only detection

    flag_9_description :

    Bad data

    flag_10_description :

    Missing data

    Array Chunk Bytes 0 B 0 B Shape (0, 596) (0, 596) Dask graph 1 chunks in 64 graph layers Data type int16 numpy.ndarray

  • mean_doppler_velocity_dealias_flag
    (time, height)
    float32
    dask.array<chunksize=(0, 596), meta=np.ndarray>

    long_name :

    Indication of whether or not dealiasing was performed on the mean_doppler_velocity

    units :

    unitless

    flag_values :

    [0 1]

    flag_meanings :

    no_dealias dealias

    flag_0_description :

    No dealiasing performed

    flag_1_description :

    Dealiasing performed

    Array Chunk Bytes 0 B 0 B Shape (0, 596) (0, 596) Dask graph 1 chunks in 64 graph layers Data type float32 numpy.ndarray

  • radar_mode_flag
    (time, height)
    float32
    dask.array<chunksize=(0, 596), meta=np.ndarray>

    long_name :

    Radar mode flag

    units :

    unitless

    flag_values :

    [0 1 2 3 4]

    flag_meanings :

    No_significant_detection High_sensitivity_mode-long_pulse Medium_sensitivity_mode-long_pulse General_mode-short_pulse Precipitation_mode-short_pulse-attenuated

    flag_0_description :

    No significant detection

    flag_1_description :

    High sensitivity mode (long pulse)

    flag_2_description :

    Medium sensitivity mode (long pulse)

    flag_3_description :

    General mode (short pulse)

    flag_4_description :

    Precipitation mode (short pulse, attenuated)

    Array Chunk Bytes 0 B 0 B Shape (0, 596) (0, 596) Dask graph 1 chunks in 64 graph layers Data type float32 numpy.ndarray

  • cloud_source_flag
    (time, height)
    int16
    dask.array<chunksize=(0, 596), meta=np.ndarray>

    long_name :

    Instrument source flag for cloud (hydrometeor) detections

    units :

    unitless

    flag_values :

    [0 1 2 3 4 5 6]

    flag_meanings :

    No_detection_due_to_missing_radar_and_micropulse_lidar_data clear_according_to_radar_and_lidar cloud_detected_by_radar_and_lidar cloud_detected_by_radar_only cloud_detected_by_lidar_only cloud_detected_by_radar_but_lidar_data_missing cloud_detected_by_lidar_but_radar_data_missing

    flag_0_description :

    No detection due to missing radar and micropulse lidar data

    flag_1_description :

    Clear according to radar and lidar

    flag_2_description :

    Cloud detected by radar and lidar

    flag_3_description :

    Cloud detected by radar only

    flag_4_description :

    Cloud detected by lidar only

    flag_5_description :

    Cloud detected by radar but lidar data missing

    flag_6_description :

    Cloud detected by lidar but radar data missing

    Array Chunk Bytes 0 B 0 B Shape (0, 596) (0, 596) Dask graph 1 chunks in 64 graph layers Data type int16 numpy.ndarray

  • precip_mean
    (time)
    float32
    dask.array<chunksize=(0,), meta=np.ndarray>

    long_name :

    Precipitation mean from rain gauge

    units :

    mm/hr

    ancillary_variables :

    qc_precip_mean

    Array Chunk Bytes 0 B 0 B Shape (0,) (0,) Dask graph 1 chunks in 64 graph layers Data type float32 numpy.ndarray

  • qc_precip_mean
    (time)
    int32
    dask.array<chunksize=(0,), meta=np.ndarray>

    long_name :

    Quality check results on field: Precipitation mean from rain gauge

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Not used

    bit_1_assessment :

    Bad

    bit_2_description :

    Not used

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 0 B 0 B Shape (0,) (0,) Dask graph 1 chunks in 64 graph layers Data type int32 numpy.ndarray

  • mwr_lwp
    (time)
    float32
    dask.array<chunksize=(0,), meta=np.ndarray>

    long_name :

    Liquid water path best-estimate from microwave radiometer

    units :

    g/m^2

    ancillary_variables :

    qc_mwr_lwp

    Array Chunk Bytes 0 B 0 B Shape (0,) (0,) Dask graph 1 chunks in 64 graph layers Data type float32 numpy.ndarray

  • qc_mwr_lwp
    (time)
    int32
    dask.array<chunksize=(0,), meta=np.ndarray>

    long_name :

    Quality check results on field: Liquid water path best-estimate from microwave radiometer

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Not used

    bit_1_assessment :

    Bad

    bit_2_description :

    Not used

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 0 B 0 B Shape (0,) (0,) Dask graph 1 chunks in 64 graph layers Data type int32 numpy.ndarray

  • radar_first_top
    (time)
    float32
    dask.array<chunksize=(0,), meta=np.ndarray>

    long_name :

    KAZR top height of lowest significant detection layer, before clutter removal

    units :

    m

    valid_range :

    [ 0. 25000.]

    flag_values :

    -1.0

    flag_meanings :

    clear_sky

    Array Chunk Bytes 0 B 0 B Shape (0,) (0,) Dask graph 1 chunks in 64 graph layers Data type float32 numpy.ndarray

  • cloud_base_best_estimate
    (time)
    float32
    dask.array<chunksize=(0,), meta=np.ndarray>

    long_name :

    Cloud base best estimate, based on ceilometer and micropulse lidar

    units :

    m

    valid_range :

    [ 0. 25000.]

    flag_values :

    [-2. -1.]

    flag_meanings :

    possible_clear_sky clear_sky

    comment :

    -2. Possible clear sky (No MPL observations available, Ceilometer obscured, but no cloud detected), -1. Clear sky, >= 0. Valid cloud base height

    Array Chunk Bytes 0 B 0 B Shape (0,) (0,) Dask graph 1 chunks in 64 graph layers Data type float32 numpy.ndarray

  • cloud_layer_base_height
    (time, layer)
    float32
    dask.array<chunksize=(0, 10), meta=np.ndarray>

    long_name :

    Base height of hydrometeor layers for up to 10 layers, based on combined radar and micropulse lidar observations

    units :

    m

    valid_range :

    [ 0. 25000.]

    flag_values :

    -1.0

    flag_meanings :

    clear_sky

    Array Chunk Bytes 0 B 0 B Shape (0, 10) (0, 10) Dask graph 1 chunks in 64 graph layers Data type float32 numpy.ndarray

  • cloud_layer_top_height
    (time, layer)
    float32
    dask.array<chunksize=(0, 10), meta=np.ndarray>

    long_name :

    Top height of hydrometeor layers for up to 10 layers, based on combined radar and micropulse lidar observations

    units :

    m

    valid_range :

    [ 0. 25000.]

    flag_values :

    -1.0

    flag_meanings :

    clear_sky

    Array Chunk Bytes 0 B 0 B Shape (0, 10) (0, 10) Dask graph 1 chunks in 64 graph layers Data type float32 numpy.ndarray

  • cloud_mask_mplzwang
    (time, height)
    float32
    dask.array<chunksize=(0, 596), meta=np.ndarray>

    long_name :

    Cloud mask from 30smpl1zwang

    units :

    unitless

    flag_values :

    [0 1 2]

    flag_meanings :

    clear cloudy mask_not_used_to_determine_cloud_boundaries_below_3km

    flag_0_description :

    clear

    flag_1_description :

    cloudy

    flag_2_description :

    Mask not used to determine cloud boundaries below 3km

    comment :

    Cloud mask indeterminate below 500 m

    Array Chunk Bytes 0 B 0 B Shape (0, 596) (0, 596) Dask graph 1 chunks in 64 graph layers Data type float32 numpy.ndarray

  • minimum_detectable_reflectivity
    (time, radar_mode, height)
    float32
    dask.array<chunksize=(0, 4, 596), meta=np.ndarray>

    long_name :

    Minimum detectable reflectivity level

    units :

    dBZ

    comment :

    Noise values are reduced by signal processing, which is dependent upon the cloud properties. Therefore actual noise levels can fluctuate with time. Reported here are mean values for the time period.

    Array Chunk Bytes 0 B 0 B Shape (0, 4, 596) (0, 4, 596) Dask graph 1 chunks in 95 graph layers Data type float32 numpy.ndarray

  • minimum_detectable_reflectivity_flag
    (time, height)
    float32
    dask.array<chunksize=(0, 596), meta=np.ndarray>

    long_name :

    Flag identifies points with reflectivity below radar expected sensitivity level

    units :

    unitless

    flag_values :

    [0 1]

    flag_meanings :

    reflectivity_exceeds_minimum_detectable_level reflectivity_below_minimum_sensitivity

    flag_0_description :

    Reflectivity exceeds minimum detectable level

    flag_1_description :

    Reflectivity below minimum sensitivity

    Array Chunk Bytes 0 B 0 B Shape (0, 596) (0, 596) Dask graph 1 chunks in 64 graph layers Data type float32 numpy.ndarray

  • reflectivity_saturation_flag
    (time, height)
    float32
    dask.array<chunksize=(0, 596), meta=np.ndarray>

    long_name :

    Flag indicating when reflectivity values are saturated

    units :

    unitless

    flag_values :

    [0 1]

    flag_meanings :

    reflectivity_not_saturated reflectivity_at_or_above_radar_saturation_level

    flag_0_description :

    Reflectivity not saturated

    flag_1_description :

    Reflectivity at or above radar saturation level

    Array Chunk Bytes 0 B 0 B Shape (0, 596) (0, 596) Dask graph 1 chunks in 64 graph layers Data type float32 numpy.ndarray

  • instrument_availability_flag
    (time)
    int16
    dask.array<chunksize=(0,), meta=np.ndarray>

    long_name :

    Indicates which instruments have data available

    units :

    unitless

    flag_masks :

    [ 1 2 4 8 16]

    flag_meanings :

    KAZR MPL Ceilometer MWR rain_gauge

    bit_1_description :

    KAZR

    bit_2_description :

    MPL

    bit_3_description :

    Ceilometer

    bit_4_description :

    MWR

    bit_5_description :

    rain gauge

    Array Chunk Bytes 0 B 0 B Shape (0,) (0,) Dask graph 1 chunks in 64 graph layers Data type int16 numpy.ndarray

  • lat
    (time)
    float32

    long_name :

    North latitude

    units :

    degree_N

    valid_min :

    -90.0

    valid_max :

    90.0

    standard_name :

    latitude

    array([], dtype=float32)

  • lon
    (time)
    float32

    long_name :

    East longitude

    units :

    degree_E

    valid_min :

    -180.0

    valid_max :

    180.0

    standard_name :

    longitude

    array([], dtype=float32)

  • alt
    (time)
    float32

    long_name :

    Altitude above mean sea level

    units :

    m

    standard_name :

    altitude

    array([], dtype=float32)

• Indexes: (4)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex([], dtype='datetime64[ns]', name='time', freq=None))

  • layer
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int32', name='layer'))

  • height
    PandasIndex

    PandasIndex(Index([  160.0,   190.0,   220.0,   250.0,   280.0,   310.0,   340.0,   370.0,
             400.0,   430.0,
           ...
           17740.0, 17770.0, 17800.0, 17830.0, 17860.0, 17890.0, 17920.0, 17950.0,
           17980.0, 18010.0],
          dtype='float32', name='height', length=596))

  • radar_mode
    PandasIndex

    PandasIndex(Index([b'hi', b'md', b'ge', b'pr'], dtype='object', name='radar_mode'))

• Attributes: (22)

  command_line :

  idl -R -n kazrcfrarscl -n kazrcfrarsclc1 -s anx -f M1 -b 20200301 --asynchronous

  Conventions :

  ARM-1.2

  process_version :

  vap-kazrcfrarscl-1.6-4.el7

  dod_version :

  arsclkazr1kollias-c1-4.0

  site_id :

  anx

  platform_id :

  arsclkazr1kollias

  facility_id :

  M1

  data_level :

  c1

  location_description :

  Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE), Andoy, Norway

  datastream :

  anxarsclkazr1kolliasM1.c1

  input_datastreams :

  anxceilM1.b1 : 1.6 : 20200301.000014 anxkazrcfrcorgeM1.c1 : 1.4 : 20200301.000000 anxkazrcfrcormdM1.c1 : 1.4 : 20200301.000000 anx30smplcmask1zwangM1.c1 : 1.0 : 20200301.000004 anxmwrret1liljclouM1.c2 : 1.6 : 20200301.000008 anxmwrlosM1.b1 : 2.3 : 20200301.000008-20200301.000105 anxmetM1.b1 : 4.41 : 20200301.000000

  radar_polarization_status :

  single polarization

  radar_modes_in_use :

  md ge

  maximum_clutter_height :

  Not applicable at this site

  radar_operating_frequency_burst :

  34.830 GHz

  radar_operating_frequency_chirp :

  34.890 GHz

  doi :

  10.5439/1228768

  history :

  created by user malynn on machine node1.adc.arm.gov at 2022-12-06 22:14:05, using vap-kazrcfrarscl-1.6-4.el7

  _file_dates :

  ['20200301', '20200302', '20200303', '20200304', '20200305', '20200306', '20200307', '20200308', '20200309', '20200310', '20200311', '20200312', '20200313', '20200314', '20200315', '20200316', '20200317', '20200318', '20200319', '20200320', '20200321', '20200322', '20200323', '20200324', '20200325', '20200326', '20200327', '20200328', '20200329', '20200330', '20200331']

  _file_times :

  ['000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000', '000000']

  _datastream :

  anxarsclkazr1kolliasM1.c1

  _arm_standards_flag :

  1

# plot reflectivity
variable='reflectivity'
#variable='reflectivity_best_estimate'
# Let's filter out test 5 using ACT.  Yes, it's that simple!
ds_arscl.qcfilter.datafilter(variable, rm_tests=[1, 2], del_qc_var=False)

# There are other ways we can filter data out as well.  Using the
# rm_assessments will filter out by all Bad/Suspect tests that are failing
# ds.qcfilter.datafilter(variable, rm_assessments=['Bad', 'Suspect'], del_qc_var=False)

# Let's check out the attributes of the variable
# Whenever data are filtered out using the datafilter function
# a comment will be added to the variable history for provenance purposes
#print(ds_arscl[variable].attrs)

# And plot it all again!
# Create a plotting display object with 2 plots
display = act.plotting.TimeSeriesDisplay(ds_arscl, figsize=(15, 10), subplot_shape=(2,))

# Plot up the variable in the first plot
#display.plot(variable, subplot_index=(0,))

ref = ds_arscl.reflectivity_best_estimate
vel = ds_arscl.mean_doppler_velocity
ref_lowest_5000m = ref.sel(height=slice(0., 5000))
vel_lowest_5000m = vel.sel(height=slice(0., 5000))
ref_lowest_5000m.plot(x='time',y='height',
                                   cmap='ChaseSpectral',
                                   vmin=-40,
                                   vmax=20)
vel_lowest_5000m.plot(x='time',y='height',
                                   cmap='seismic',
                                   vmin=-40,
                                   vmax=20)

#ref_lowest_5000m.hvplot(x='time',
#                                     y='height',
#                                     cmap='ChaseSpectral',
#                                     clim=(-40, 20),
#                                     rasterize=True)

# Plot cloud base height
#display.plot('cloud_base_best_estimate', subplot_index=(0,))

# Plot velocities 
#display.plot('mean_doppler_velocity', subplot_index=(1,))
plt.show()

formatter = DatetimeTickFormatter(hours="%d %b %Y \n %H:%M UTC")
reflectivity_plot = ds_arscl.reflectivity.sel(height=slice(0, 7000)).hvplot(x='time', y='height', cmap='Spectral_r', xformatter=formatter, clim=(-40, 20), rasterize=True, clabel='Reflectivity (dBZ)')
velocity_plot = ds_arscl.mean_doppler_velocity.sel(height=slice(0, 7000)).hvplot(x='time', y='height', cmap='seismic', xformatter=formatter, clim=(-5, 5), rasterize=True, clabel='Mean Doppler Velocity (m/s)')

reflectivity_plot + velocity_plot

# NSA datastream
datastream = 'nsaarsclkazr1kolliasC1.c0'
startdate = '2016-11-05'
enddate = '2016-11-06'

# Read in data
result = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)

ds_arscl_nsa = act.io.read_arm_netcdf(result)

ds_arscl_nsa

[DOWNLOADING] nsaarsclkazr1kolliasC1.c0.20161106.000000.nc
[DOWNLOADING] nsaarsclkazr1kolliasC1.c0.20161105.000000.nc

If you use these data to prepare a publication, please cite:

Johnson, K., Giangrande, S., & Toto, T. Active Remote Sensing of CLouds (ARSCL)
product using Ka-band ARM Zenith Radars (ARSCLKAZR1KOLLIAS). Atmospheric
Radiation Measurement (ARM) User Facility. https://doi.org/10.5439/1393437

<xarray.Dataset> Size: 2GB
Dimensions:                               (time: 43200, height: 596, layer: 10,
                                           radar_mode: 4)
Coordinates:
  * time                                  (time) datetime64[ns] 346kB 2016-11...
  * layer                                 (layer) int32 40B 0 1 2 3 4 5 6 7 8 9
  * height                                (height) float32 2kB 160.0 ... 1.80...
  * radar_mode                            (radar_mode) |S2 8B b'hi' ... b'pr'
Data variables: (12/33)
    base_time                             (time) datetime64[ns] 346kB 2016-11...
    time_offset                           (time) datetime64[ns] 346kB 2016-11...
    reflectivity_best_estimate            (time, height) float32 103MB dask.array<chunksize=(901, 596), meta=np.ndarray>
    qc_reflectivity_best_estimate         (time, height) int32 103MB dask.array<chunksize=(901, 596), meta=np.ndarray>
    reflectivity                          (time, height) float32 103MB dask.array<chunksize=(901, 596), meta=np.ndarray>
    qc_reflectivity                       (time, height) int32 103MB dask.array<chunksize=(901, 596), meta=np.ndarray>
    ...                                    ...
    minimum_detectable_reflectivity_flag  (time, height) float32 103MB dask.array<chunksize=(901, 596), meta=np.ndarray>
    reflectivity_saturation_flag          (time, height) float32 103MB dask.array<chunksize=(901, 596), meta=np.ndarray>
    instrument_availability_flag          (time) int16 86kB dask.array<chunksize=(1,), meta=np.ndarray>
    lat                                   (time) float32 173kB 71.32 ... 71.32
    lon                                   (time) float32 173kB -156.6 ... -156.6
    alt                                   (time) float32 173kB 8.0 8.0 ... 8.0
Attributes: (12/23)
    command_line:                     idl -R -n kazrarsclc0 -s nsa -f C1 -b 2...
    Conventions:                      ARM-1.2
    process_version:                  vap-kazrarscl-1.0.0-devel
    dod_version:                      arsclkazr1kollias-c0-1.0
    site_id:                          nsa
    platform_id:                      arsclkazr1kollias
    ...                               ...
    doi:                              10.5439/1393437
    history:                          created by user ttoto on machine talc.d...
    _file_dates:                      ['20161105', '20161106']
    _file_times:                      ['000000', '000000']
    _datastream:                      nsaarsclkazr1kolliasC1.c0
    _arm_standards_flag:              1

xarray.Dataset

• Dimensions:
  • time: 43200
  • height: 596
  • layer: 10
  • radar_mode: 4
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2016-11-05 ... 2016-11-06T23:59:56

    long_name :

    Time offset from midnight

    standard_name :

    time

    array(['2016-11-05T00:00:00.000000000', '2016-11-05T00:00:04.000000000',
           '2016-11-05T00:00:08.000000000', ..., '2016-11-06T23:59:48.000000000',
           '2016-11-06T23:59:52.000000000', '2016-11-06T23:59:56.000000000'],
          dtype='datetime64[ns]')

  • layer
    (layer)
    int32
    0 1 2 3 4 5 6 7 8 9

    long_name :

    Cloud layer number

    units :

    unitless

    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int32)

  • height
    (height)
    float32
    160.0 190.0 ... 1.798e+04 1.801e+04

    long_name :

    Height above ground level

    units :

    m

    standard_name :

    height

    array([  160.,   190.,   220., ..., 17950., 17980., 18010.], dtype=float32)

  • radar_mode
    (radar_mode)
    |S2
    b'hi' b'md' b'ge' b'pr'

    long_name :

    Radar mode names

    units :

    unitless

    comment :

    Only two radar modes are in use simultaneously. See global attribute radar_modes_in_use for list of modes used

    array([b'hi', b'md', b'ge', b'pr'], dtype='|S2')

• Data variables: (33)
  • base_time
    (time)
    datetime64[ns]
    2016-11-05 ... 2016-11-06

    string :

    2016-11-05 00:00:00 0:00

    long_name :

    Base time in Epoch

    ancillary_variables :

    time_offset

    array(['2016-11-05T00:00:00.000000000', '2016-11-05T00:00:00.000000000',
           '2016-11-05T00:00:00.000000000', ...,
           '2016-11-06T00:00:00.000000000', '2016-11-06T00:00:00.000000000',
           '2016-11-06T00:00:00.000000000'], dtype='datetime64[ns]')

  • time_offset
    (time)
    datetime64[ns]
    2016-11-05 ... 2016-11-06T23:59:56

    long_name :

    Time offset from base_time

    ancillary_variables :

    base_time

    array(['2016-11-05T00:00:00.000000000', '2016-11-05T00:00:04.000000000',
           '2016-11-05T00:00:08.000000000', ...,
           '2016-11-06T23:59:48.000000000', '2016-11-06T23:59:52.000000000',
           '2016-11-06T23:59:56.000000000'], dtype='datetime64[ns]')

  • reflectivity_best_estimate
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Best-estimate reflectivity

    units :

    dBZ

    ancillary_variables :

    qc_reflectivity_best_estimate

    valid_min :

    -90.0

    valid_max :

    50.0

    resolution :

    0.001

    comment1 :

    Contains data from best radar operating mode (see radar_mode_flag); data points with non-missing values contain hydrometeors or both hydrometeors and clutter (see reflectivity_clutter_flag)

    comment2 :

    Reflectivity values have not yet been calibrated. Use with caution.

    Array Chunk Bytes 98.22 MiB 2.05 MiB Shape (43200, 596) (901, 596) Dask graph 48 chunks in 5 graph layers Data type float32 numpy.ndarray

  • qc_reflectivity_best_estimate
    (time, height)
    int32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Quality check results on field: Best-estimate reflectivity

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is less than the valid_min.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is greater than the valid_max.

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 98.22 MiB 2.05 MiB Shape (43200, 596) (901, 596) Dask graph 48 chunks in 5 graph layers Data type int32 numpy.ndarray

  • reflectivity
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Reflectivity

    units :

    dBZ

    ancillary_variables :

    qc_reflectivity

    valid_min :

    -90.0

    valid_max :

    50.0

    resolution :

    0.001

    comment1 :

    Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter

    comment2 :

    Reflectivity values have not yet been calibrated. Use with caution.

    Array Chunk Bytes 98.22 MiB 2.05 MiB Shape (43200, 596) (901, 596) Dask graph 48 chunks in 5 graph layers Data type float32 numpy.ndarray

  • qc_reflectivity
    (time, height)
    int32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Quality check results on field: Reflectivity

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is less than the valid_min.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is greater than the valid_max.

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 98.22 MiB 2.05 MiB Shape (43200, 596) (901, 596) Dask graph 48 chunks in 5 graph layers Data type int32 numpy.ndarray

  • mean_doppler_velocity
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Mean Doppler velocity

    units :

    m/s

    ancillary_variables :

    qc_mean_doppler_velocity

    valid_min :

    -25.0

    valid_max :

    25.0

    resolution :

    0.001

    positive :

    up

    comment :

    Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter

    Array Chunk Bytes 98.22 MiB 2.05 MiB Shape (43200, 596) (901, 596) Dask graph 48 chunks in 5 graph layers Data type float32 numpy.ndarray

  • qc_mean_doppler_velocity
    (time, height)
    int32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Quality check results on field: Mean Doppler velocity

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is less than the valid_min.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is greater than the valid_max.

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 98.22 MiB 2.05 MiB Shape (43200, 596) (901, 596) Dask graph 48 chunks in 5 graph layers Data type int32 numpy.ndarray

  • spectral_width
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Spectral width

    units :

    m/s

    ancillary_variables :

    qc_spectral_width

    valid_min :

    0.0

    valid_max :

    10.0

    resolution :

    0.001

    comment :

    Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter

    Array Chunk Bytes 98.22 MiB 2.05 MiB Shape (43200, 596) (901, 596) Dask graph 48 chunks in 5 graph layers Data type float32 numpy.ndarray

  • qc_spectral_width
    (time, height)
    int32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Quality check results on field: Spectral width

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is less than the valid_min.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is greater than the valid_max.

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 98.22 MiB 2.05 MiB Shape (43200, 596) (901, 596) Dask graph 48 chunks in 5 graph layers Data type int32 numpy.ndarray

  • linear_depolarization_ratio
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Linear depolarization ratio

    units :

    dBZ

    ancillary_variables :

    qc_linear_depolarization_ratio

    valid_min :

    -50.0

    valid_max :

    50.0

    comment :

    Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter

    Array Chunk Bytes 98.22 MiB 2.05 MiB Shape (43200, 596) (901, 596) Dask graph 48 chunks in 5 graph layers Data type float32 numpy.ndarray

  • qc_linear_depolarization_ratio
    (time, height)
    int32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Quality check results on field: Linear depolarization ratio

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is less than the valid_min.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is greater than the valid_max.

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 98.22 MiB 2.05 MiB Shape (43200, 596) (901, 596) Dask graph 48 chunks in 5 graph layers Data type int32 numpy.ndarray

  • signal_to_noise_ratio
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Signal-to-noise ratio

    units :

    dB

    resolution :

    0.001

    comment :

    Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter

    Array Chunk Bytes 98.22 MiB 2.05 MiB Shape (43200, 596) (901, 596) Dask graph 48 chunks in 5 graph layers Data type float32 numpy.ndarray

  • reflectivity_clutter_flag
    (time, height)
    int16
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Reflectivity clutter flag

    units :

    unitless

    flag_values :

    [ 0 1 2 3 9 10]

    flag_meanings :

    No_significant_detection Hydrometeor-only_detection Both_hydrometeor_and_clutter_detection Clutter-only_detection Bad_data Missing_data

    flag_0_description :

    No significant detection

    flag_1_description :

    Hydrometeor-only detection

    flag_2_description :

    Both hydrometeor and clutter detection

    flag_3_description :

    Clutter-only detection

    flag_9_description :

    Bad data

    flag_10_description :

    Missing data

    Array Chunk Bytes 49.11 MiB 1.02 MiB Shape (43200, 596) (901, 596) Dask graph 48 chunks in 5 graph layers Data type int16 numpy.ndarray

  • mean_doppler_velocity_dealias_flag
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Indication of whether or not dealiasing was performed on the mean_doppler_velocity

    units :

    unitless

    flag_values :

    [0 1]

    flag_meanings :

    no_dealias dealias

    flag_0_description :

    No dealiasing performed

    flag_1_description :

    Dealiasing performed

    Array Chunk Bytes 98.22 MiB 2.05 MiB Shape (43200, 596) (901, 596) Dask graph 48 chunks in 5 graph layers Data type float32 numpy.ndarray

  • radar_mode_flag
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Radar mode flag

    units :

    unitless

    flag_values :

    [0 1 2 3 4]

    flag_meanings :

    No_significant_detection High_sensitivity_mode-long_pulse Medium_sensitivity_mode-long_pulse General_mode-short_pulse Precipitation_mode-short_pulse-attenuated

    flag_0_description :

    No significant detection

    flag_1_description :

    High sensitivity mode (long pulse)

    flag_2_description :

    Medium sensitivity mode (long pulse)

    flag_3_description :

    General mode (short pulse)

    flag_4_description :

    Precipitation mode (short pulse, attenuated)

    Array Chunk Bytes 98.22 MiB 2.05 MiB Shape (43200, 596) (901, 596) Dask graph 48 chunks in 5 graph layers Data type float32 numpy.ndarray

  • cloud_source_flag
    (time, height)
    int16
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Instrument source flag for cloud (hydrometeor) detections

    units :

    unitless

    flag_values :

    [0 1 2 3 4 5 6]

    flag_meanings :

    No_detection_due_to_missing_radar_and_micropulse_lidar_data clear_according_to_radar_and_lidar cloud_detected_by_radar_and_lidar cloud_detected_by_radar_only cloud_detected_by_lidar_only cloud_detected_by_radar_but_lidar_data_missing cloud_detected_by_lidar_but_radar_data_missing

    flag_0_description :

    No detection due to missing radar and micropulse lidar data

    flag_1_description :

    Clear according to radar and lidar

    flag_2_description :

    Cloud detected by radar and lidar

    flag_3_description :

    Cloud detected by radar only

    flag_4_description :

    Cloud detected by lidar only

    flag_5_description :

    Cloud detected by radar but lidar data missing

    flag_6_description :

    Cloud detected by lidar but radar data missing

    Array Chunk Bytes 49.11 MiB 1.02 MiB Shape (43200, 596) (901, 596) Dask graph 48 chunks in 5 graph layers Data type int16 numpy.ndarray

  • precip_mean
    (time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    long_name :

    Precipitation mean from rain gauge

    units :

    mm/hr

    ancillary_variables :

    qc_precip_mean

    Array Chunk Bytes 168.75 kiB 4 B Shape (43200,) (1,) Dask graph 43200 chunks in 5 graph layers Data type float32 numpy.ndarray

  • qc_precip_mean
    (time)
    int32
    dask.array<chunksize=(1,), meta=np.ndarray>

    long_name :

    Quality check results on field: Precipitation mean from rain gauge

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Not used

    bit_1_assessment :

    Bad

    bit_2_description :

    Not used

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 168.75 kiB 4 B Shape (43200,) (1,) Dask graph 43200 chunks in 5 graph layers Data type int32 numpy.ndarray

  • mwr_lwp
    (time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    long_name :

    Liquid water path best-estimate from microwave radiometer

    units :

    g/m^2

    ancillary_variables :

    qc_mwr_lwp

    Array Chunk Bytes 168.75 kiB 4 B Shape (43200,) (1,) Dask graph 43200 chunks in 5 graph layers Data type float32 numpy.ndarray

  • qc_mwr_lwp
    (time)
    int32
    dask.array<chunksize=(1,), meta=np.ndarray>

    long_name :

    Quality check results on field: Liquid water path best-estimate from microwave radiometer

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Not used

    bit_1_assessment :

    Bad

    bit_2_description :

    Not used

    bit_2_assessment :

    Bad

    bit_3_description :

    Data value not available in input file, data value has been set to missing_value.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 168.75 kiB 4 B Shape (43200,) (1,) Dask graph 43200 chunks in 5 graph layers Data type int32 numpy.ndarray

  • radar_first_top
    (time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    long_name :

    KAZR top height of lowest significant detection layer, before clutter removal

    units :

    m

    valid_range :

    [ 0. 25000.]

    flag_values :

    -1.0

    flag_meanings :

    clear_sky

    Array Chunk Bytes 168.75 kiB 4 B Shape (43200,) (1,) Dask graph 43200 chunks in 5 graph layers Data type float32 numpy.ndarray

  • cloud_base_best_estimate
    (time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    long_name :

    Cloud base best estimate, based on ceilometer and micropulse lidar

    units :

    m

    valid_range :

    [ 0. 25000.]

    flag_values :

    [-2. -1.]

    flag_meanings :

    possible_clear_sky clear_sky

    comment :

    -2. Possible clear sky (No MPL observations available, Ceilometer obscured, but no cloud detected), -1. Clear sky, >= 0. Valid cloud base height

    Array Chunk Bytes 168.75 kiB 4 B Shape (43200,) (1,) Dask graph 43200 chunks in 5 graph layers Data type float32 numpy.ndarray

  • cloud_layer_base_height
    (time, layer)
    float32
    dask.array<chunksize=(1, 10), meta=np.ndarray>

    long_name :

    Base height of hydrometeor layers for up to 10 layers, based on combined radar and micropulse lidar observations

    units :

    m

    valid_range :

    [ 0. 25000.]

    flag_values :

    -1.0

    flag_meanings :

    clear_sky

    Array Chunk Bytes 1.65 MiB 40 B Shape (43200, 10) (1, 10) Dask graph 43200 chunks in 5 graph layers Data type float32 numpy.ndarray

  • cloud_layer_top_height
    (time, layer)
    float32
    dask.array<chunksize=(1, 10), meta=np.ndarray>

    long_name :

    Top height of hydrometeor layers for up to 10 layers, based on combined radar and micropulse lidar observations

    units :

    m

    valid_range :

    [ 0. 25000.]

    flag_values :

    -1.0

    flag_meanings :

    clear_sky

    Array Chunk Bytes 1.65 MiB 40 B Shape (43200, 10) (1, 10) Dask graph 43200 chunks in 5 graph layers Data type float32 numpy.ndarray

  • cloud_mask_mplzwang
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Cloud mask from 30smpl1zwang

    units :

    unitless

    flag_values :

    [0 1 2]

    flag_meanings :

    clear cloudy mask_not_used_to_determine_cloud_boundaries_below_3km

    flag_0_description :

    clear

    flag_1_description :

    cloudy

    flag_2_description :

    Mask not used to determine cloud boundaries below 3km

    comment :

    Cloud mask indeterminate below 500 m

    Array Chunk Bytes 98.22 MiB 2.05 MiB Shape (43200, 596) (901, 596) Dask graph 48 chunks in 5 graph layers Data type float32 numpy.ndarray

  • minimum_detectable_reflectivity
    (time, radar_mode, height)
    float32
    dask.array<chunksize=(21600, 4, 596), meta=np.ndarray>

    long_name :

    Minimum detectable reflectivity level

    units :

    dBZ

    comment :

    Noise values are reduced by signal processing, which is dependent upon the cloud properties. Therefore actual noise levels can fluctuate with time. Reported here are mean values for the time period.

    Array Chunk Bytes 392.87 MiB 196.44 MiB Shape (43200, 4, 596) (21600, 4, 596) Dask graph 2 chunks in 7 graph layers Data type float32 numpy.ndarray

  • minimum_detectable_reflectivity_flag
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Flag identifies points with reflectivity below radar expected sensitivity level

    units :

    unitless

    flag_values :

    [0 1]

    flag_meanings :

    reflectivity_exceeds_minimum_detectable_level reflectivity_below_minimum_sensitivity

    flag_0_description :

    Reflectivity exceeds minimum detectable level

    flag_1_description :

    Reflectivity below minimum sensitivity

    Array Chunk Bytes 98.22 MiB 2.05 MiB Shape (43200, 596) (901, 596) Dask graph 48 chunks in 5 graph layers Data type float32 numpy.ndarray

  • reflectivity_saturation_flag
    (time, height)
    float32
    dask.array<chunksize=(901, 596), meta=np.ndarray>

    long_name :

    Flag indicating when reflectivity values are saturated

    units :

    unitless

    flag_values :

    [0 1]

    flag_meanings :

    reflectivity_not_saturated reflectivity_at_or_above_radar_saturation_level

    flag_0_description :

    Reflectivity not saturated

    flag_1_description :

    Reflectivity at or above radar saturation level

    Array Chunk Bytes 98.22 MiB 2.05 MiB Shape (43200, 596) (901, 596) Dask graph 48 chunks in 5 graph layers Data type float32 numpy.ndarray

  • instrument_availability_flag
    (time)
    int16
    dask.array<chunksize=(1,), meta=np.ndarray>

    long_name :

    Indicates which instruments have data available

    units :

    unitless

    flag_masks :

    [ 1 2 4 8 16]

    flag_meanings :

    KAZR MPL Ceilometer MWR rain_gauge

    bit_1_description :

    KAZR

    bit_2_description :

    MPL

    bit_3_description :

    Ceilometer

    bit_4_description :

    MWR

    bit_5_description :

    rain gauge

    Array Chunk Bytes 84.38 kiB 2 B Shape (43200,) (1,) Dask graph 43200 chunks in 5 graph layers Data type int16 numpy.ndarray

  • lat
    (time)
    float32
    71.32 71.32 71.32 ... 71.32 71.32

    long_name :

    North latitude

    units :

    degree_N

    valid_min :

    -90.0

    valid_max :

    90.0

    standard_name :

    latitude

    array([71.323, 71.323, 71.323, ..., 71.323, 71.323, 71.323], dtype=float32)

  • lon
    (time)
    float32
    -156.6 -156.6 ... -156.6 -156.6

    long_name :

    East longitude

    units :

    degree_E

    valid_min :

    -180.0

    valid_max :

    180.0

    standard_name :

    longitude

    array([-156.609, -156.609, -156.609, ..., -156.609, -156.609, -156.609],
          dtype=float32)

  • alt
    (time)
    float32
    8.0 8.0 8.0 8.0 ... 8.0 8.0 8.0 8.0

    long_name :

    Altitude above mean sea level

    units :

    m

    standard_name :

    altitude

    array([8., 8., 8., ..., 8., 8., 8.], dtype=float32)

• Indexes: (4)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2016-11-05 00:00:00', '2016-11-05 00:00:04',
                   '2016-11-05 00:00:08', '2016-11-05 00:00:12',
                   '2016-11-05 00:00:16', '2016-11-05 00:00:20',
                   '2016-11-05 00:00:24', '2016-11-05 00:00:28',
                   '2016-11-05 00:00:32', '2016-11-05 00:00:36',
                   ...
                   '2016-11-06 23:59:20', '2016-11-06 23:59:24',
                   '2016-11-06 23:59:28', '2016-11-06 23:59:32',
                   '2016-11-06 23:59:36', '2016-11-06 23:59:40',
                   '2016-11-06 23:59:44', '2016-11-06 23:59:48',
                   '2016-11-06 23:59:52', '2016-11-06 23:59:56'],
                  dtype='datetime64[ns]', name='time', length=43200, freq=None))

  • layer
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int32', name='layer'))

  • height
    PandasIndex

    PandasIndex(Index([  160.0,   190.0,   220.0,   250.0,   280.0,   310.0,   340.0,   370.0,
             400.0,   430.0,
           ...
           17740.0, 17770.0, 17800.0, 17830.0, 17860.0, 17890.0, 17920.0, 17950.0,
           17980.0, 18010.0],
          dtype='float32', name='height', length=596))

  • radar_mode
    PandasIndex

    PandasIndex(Index([b'hi', b'md', b'ge', b'pr'], dtype='object', name='radar_mode'))

• Attributes: (23)

  command_line :

  idl -R -n kazrarsclc0 -s nsa -f C1 -b 20161105 -n kazrarsclc0 -R --asynchronous

  Conventions :

  ARM-1.2

  process_version :

  vap-kazrarscl-1.0.0-devel

  dod_version :

  arsclkazr1kollias-c0-1.0

  site_id :

  nsa

  platform_id :

  arsclkazr1kollias

  facility_id :

  C1

  data_level :

  c0

  location_description :

  North Slope of Alaska (NSA), Barrow, Alaska

  datastream :

  nsaarsclkazr1kolliasC1.c0

  input_datastreams :

  nsaceilC1.b1 : 1.3 : 20161105.000005 nsakazrcorgeC1.c0 : 5.0 : 20161105.000000 nsakazrcormdC1.c0 : 5.0 : 20161105.000000 nsa30smplcmask1zwangC1.c1 : 0.6 : 20161105.000009 nsamwrret1liljclouC1.c1 : 1.77 : 20161105.000000 nsametC1.b1 : 4.35 : 20161105.000000

  radar_polarization_status :

  dual polarization

  radar_modes_in_use :

  md ge

  maximum_clutter_height :

  Not applicable at nsa

  radar_operating_frequency_burst :

  34.830000 GHz

  radar_operating_frequency_chirp :

  34.890000 GHz

  comment :

  Reflectivity values have not yet been calibrated. Use with caution.

  doi :

  10.5439/1393437

  history :

  created by user ttoto on machine talc.dmf.arm.gov at 2017-11-16 06:14:40, using vap-kazrarscl-1.0.0-devel

  _file_dates :

  ['20161105', '20161106']

  _file_times :

  ['000000', '000000']

  _datastream :

  nsaarsclkazr1kolliasC1.c0

  _arm_standards_flag :

  1

reflectivity_plot = ds_arscl_nsa.reflectivity.sel(height=slice(0, 3000)).hvplot(x='time', y='height', cmap='Spectral_r', xformatter=formatter, clim=(-40, 20), rasterize=True, clabel='Reflectivity (dBZ)')
velocity_plot = ds_arscl_nsa.mean_doppler_velocity.sel(height=slice(0, 3000)).hvplot(x='time', y='height', cmap='seismic', xformatter=formatter, clim=(-5, 5), rasterize=True, clabel='Mean Doppler Velocity (m/s)')
specwidth_plot = ds_arscl_nsa.spectral_width.sel(height=slice(0, 3000)).hvplot(x='time', y='height', cmap='seismic', xformatter=formatter, clim=(-1, 1), rasterize=True, clabel='Spectral Width (m/s)')

reflectivity_plot + velocity_plot + specwidth_plot

# COMBLE PBL heights
datastream = 'anxpblhtsonde1mcfarlM1.c1'
datastream2 = 'nsapblhtsonde1mcfarlC1.c1'

startdate = '2020-03-01'
enddate = '2020-03-31'

startdate2 = '2016-11-05'
enddate2 = '2016-11-06'

# Read in data
result = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)
result2 = act.discovery.download_arm_data(username, token, datastream2, startdate2, enddate2)

ds_pbl_comble = act.io.read_arm_netcdf(result)
ds_pbl_nsa = act.io.read_arm_netcdf(result2)

[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200311.052700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200312.172800.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200321.232600.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200305.052800.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200330.052500.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200313.232200.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200325.172400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200314.053000.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200322.053100.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200306.172900.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200305.172300.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200326.232600.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200315.112300.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200315.172700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200313.052700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200301.112300.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200321.172900.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200329.172800.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200314.173400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200328.232200.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200304.172800.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200319.112100.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200311.234900.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200303.232400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200310.172400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200326.112700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200327.232900.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200326.052600.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200303.112600.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200314.232400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200317.172700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200310.232700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200309.112400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200320.112700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200329.053400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200329.232800.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200309.173000.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200307.112700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200301.172900.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200327.052700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200316.054100.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200323.112500.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200302.172500.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200305.232500.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200325.232400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200319.052700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200318.232700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200320.232400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200319.172200.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200318.112500.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200304.112400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200308.052800.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200309.232800.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200330.112500.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200305.112700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200318.173100.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200310.053200.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200329.112300.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200320.053000.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200330.172600.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200313.112600.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200312.052700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200325.053300.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200306.052800.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200322.173000.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200322.112400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200308.172900.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200327.112300.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200327.172700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200317.112600.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200328.112300.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200324.052800.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200304.052900.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200321.112600.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200314.112400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200318.052700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200319.232800.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200320.054500.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200307.232600.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200328.172600.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200322.232700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200311.112200.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200303.053600.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200312.112700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200323.173500.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200330.233500.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200321.052600.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200320.174700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200325.112400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200307.172900.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200326.172400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200328.053500.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200315.052900.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200313.172600.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200310.112700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200324.112600.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200308.232700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200302.052300.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200303.172500.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200311.174700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200324.232400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200307.052500.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200306.232500.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200302.112400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200323.052800.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200316.232600.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200324.172400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200317.052600.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200317.232500.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200306.112700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200316.172800.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200304.232400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200301.052700.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200301.232400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200315.232400.cdf
[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200323.232300.cdf

If you use these data to prepare a publication, please cite:

Zhang, D., & Zhang, D. Planetary Boundary Layer Height (PBLHTSONDE1MCFARL).
Atmospheric Radiation Measurement (ARM) User Facility.
https://doi.org/10.5439/1991783

[DOWNLOADING] nsapblhtsonde1mcfarlC1.c1.20161105.173000.cdf
[DOWNLOADING] nsapblhtsonde1mcfarlC1.c1.20161105.053000.cdf

If you use these data to prepare a publication, please cite:

Riihimaki, L., Riihimaki, L., Zhang, D., & Zhang, D. Planetary Boundary Layer
Height (PBLHTSONDE1MCFARL). Atmospheric Radiation Measurement (ARM) User
Facility. https://doi.org/10.5439/1991783

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[93], line 15
     12 result = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)
     13 result2 = act.discovery.download_arm_data(username, token, datastream2, startdate2, enddate2)
---> 15 ds_pbl_comble = act.io.read_arm_netcdf(result)
     16 ds_pbl_nsa = act.io.read_arm_netcdf(result2)

File /opt/conda/lib/python3.11/site-packages/act/io/arm.py:172, in read_arm_netcdf(filenames, concat_dim, return_None, combine, decode_times, use_cftime, use_base_time, combine_attrs, cleanup_qc, keep_variables, **kwargs)
    168         ds = xr.open_mfdataset(filenames, **kwargs)
    170     else:
    171         # When all else fails raise the orginal exception
--> 172         raise exception
    174 # If requested use base_time and time_offset to derive time. Assumes that the units
    175 # of both are in seconds and that the value is number of seconds since epoch.
    176 if use_base_time:

File /opt/conda/lib/python3.11/site-packages/act/io/arm.py:147, in read_arm_netcdf(filenames, concat_dim, return_None, combine, decode_times, use_cftime, use_base_time, combine_attrs, cleanup_qc, keep_variables, **kwargs)
    143     except_tuple = except_tuple + (FileNotFoundError, OSError)
    145 try:
    146     # Read data file with Xarray function
--> 147     ds = xr.open_mfdataset(filenames, **kwargs)
    149 except except_tuple as exception:
    150     # If requested return None for File not found error
    151     if type(exception).__name__ == 'FileNotFoundError':

File /opt/conda/lib/python3.11/site-packages/xarray/backends/api.py:1082, in open_mfdataset(paths, chunks, concat_dim, compat, preprocess, engine, data_vars, coords, combine, parallel, join, attrs_file, combine_attrs, **kwargs)
   1069     combined = _nested_combine(
   1070         datasets,
   1071         concat_dims=concat_dim,
   (...)
   1077         combine_attrs=combine_attrs,
   1078     )
   1079 elif combine == "by_coords":
   1080     # Redo ordering from coordinates, ignoring how they were ordered
   1081     # previously
-> 1082     combined = combine_by_coords(
   1083         datasets,
   1084         compat=compat,
   1085         data_vars=data_vars,
   1086         coords=coords,
   1087         join=join,
   1088         combine_attrs=combine_attrs,
   1089     )
   1090 else:
   1091     raise ValueError(
   1092         f"{combine} is an invalid option for the keyword argument"
   1093         " ``combine``"
   1094     )

File /opt/conda/lib/python3.11/site-packages/xarray/core/combine.py:958, in combine_by_coords(data_objects, compat, data_vars, coords, fill_value, join, combine_attrs)
    954     grouped_by_vars = itertools.groupby(sorted_datasets, key=vars_as_keys)
    956     # Perform the multidimensional combine on each group of data variables
    957     # before merging back together
--> 958     concatenated_grouped_by_data_vars = tuple(
    959         _combine_single_variable_hypercube(
    960             tuple(datasets_with_same_vars),
    961             fill_value=fill_value,
    962             data_vars=data_vars,
    963             coords=coords,
    964             compat=compat,
    965             join=join,
    966             combine_attrs=combine_attrs,
    967         )
    968         for vars, datasets_with_same_vars in grouped_by_vars
    969     )
    971 return merge(
    972     concatenated_grouped_by_data_vars,
    973     compat=compat,
   (...)
    976     combine_attrs=combine_attrs,
    977 )

File /opt/conda/lib/python3.11/site-packages/xarray/core/combine.py:959, in <genexpr>(.0)
    954     grouped_by_vars = itertools.groupby(sorted_datasets, key=vars_as_keys)
    956     # Perform the multidimensional combine on each group of data variables
    957     # before merging back together
    958     concatenated_grouped_by_data_vars = tuple(
--> 959         _combine_single_variable_hypercube(
    960             tuple(datasets_with_same_vars),
    961             fill_value=fill_value,
    962             data_vars=data_vars,
    963             coords=coords,
    964             compat=compat,
    965             join=join,
    966             combine_attrs=combine_attrs,
    967         )
    968         for vars, datasets_with_same_vars in grouped_by_vars
    969     )
    971 return merge(
    972     concatenated_grouped_by_data_vars,
    973     compat=compat,
   (...)
    976     combine_attrs=combine_attrs,
    977 )

File /opt/conda/lib/python3.11/site-packages/xarray/core/combine.py:619, in _combine_single_variable_hypercube(datasets, fill_value, data_vars, coords, compat, join, combine_attrs)
    613 if len(datasets) == 0:
    614     raise ValueError(
    615         "At least one Dataset is required to resolve variable names "
    616         "for combined hypercube."
    617     )
--> 619 combined_ids, concat_dims = _infer_concat_order_from_coords(list(datasets))
    621 if fill_value is None:
    622     # check that datasets form complete hypercube
    623     _check_shape_tile_ids(combined_ids)

File /opt/conda/lib/python3.11/site-packages/xarray/core/combine.py:111, in _infer_concat_order_from_coords(datasets)
    109     ascending = False
    110 else:
--> 111     raise ValueError(
    112         f"Coordinate variable {dim} is neither "
    113         "monotonically increasing nor "
    114         "monotonically decreasing on all datasets"
    115     )
    117 # Assume that any two datasets whose coord along dim starts
    118 # with the same value have the same coord values throughout.
    119 if any(index.size == 0 for index in indexes):

ValueError: Coordinate variable height_ss is neither monotonically increasing nor monotonically decreasing on all datasets

ds_pbl_nsa

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[94], line 1
----> 1 ds_pbl_nsa

NameError: name 'ds_pbl_nsa' is not defined